Interaction, Innovation and Incubation

Financial Analytics Training

January 31st, 2017 - March 31st, 2017

The Financial Analytics Training program uses PERACTON® financial analytics
platform (MAARS) to train students in risk assessment, algorithmic trading
and portfolio management using diverse instruments of stocks, bonds and ETFs.
PERACTON’s back-testing suite powered by Python will be used to train
students to develop their own novel trading strategies.

Week 2

7th Feb and 9th Feb

Learn how to build own search strategies in MAARS ranking system:
3 Long and 3 Short and save for future

Back-test buy and hold/sell and hold in an open system like
Yahoo Finance or Google finance for 1 or 2 years duration

Change your strategies’ variables and settings in order to
observe the change in returns

Improve these strategies continuously during the course’s
lifetime

(Note: full professional search strategies will be made available later
during the course)

Course Content:

No content available.

Instructors:

Lorna (Ec. NUI Galway), Manasawee (Ec. NUI Galway)

Week 3

14th and 16th Feb

Backtesting in Python

Basic concepts – ‘how to/why’

Explanation of zipline API

Build a very simple python back-testing

Try to improve the functionality of back-testing

Calculate the number of shares to buy based upon personal risk profile

Course Content:

No content available.

Instructors:

Lucas (Insight. NUI Galway)

Week 4

21st Feb and 23rd Feb

First backtesting algos in Python

Hands on experiment 4.1

Build Long and Hold Python algo that maximize revenues and
minimize costs

a) Picks stock manually NOT using MAARS

b) Pick stocks manually USING MAARS

No profit limit

Build stop loss

Take into account slippage and commission costs

Calculate the number of shares to buy

Compare the difference between a) and b) returns

Hands on experiment 4.2

Build Short and Hold Python algo that maximize revenues and minimize costs

a) Picks stock manually NOT using MAARS

b) Pick stocks manually USING MAARS

No profit limit

Build stop loss

Take into account slippage and commission costs

Calculate the number of shares to buy

Compare the difference between a) and b) returns

Course Content:

No content available.

Instructors:

Lucas (Insight. NUI Galway), David (NUI Galway)

Week 5

28th Feb and 2nd Mar

Backtesting in Python

Hands on experiment 5.1a

Build both Long/Short and Hold Python algo that maximize
revenues and minimize costs

Picks stock manually using MAARS ranking

No profit limit

Build stop loss

Take into account slippage and commission costs

Calculate the number of shares to buy

Hands on experiment 5.1b

Relative value trading: find 2 similar stocks (same industry,
same product, close prices) and then apply a similar
algorithm as 5.1 a) with buy/sell orders when the prices of
the stocks start to diverge more than usual.

Week 6

7th Mar and 9th Mar

Play with variations of strategies in MAARS and then call them from
Python

Experiment with commission and slippage

Experiment with SSIX social sentiment data by adding it to MAARS
strategies: run back-testing with and without SSIX data and see if
there are improvements or not in your returns

Experiment with custom start/end back-testing

Course Content:

No content available.

Instructors:

Lucas (Insight. NUI Galway), Manasawee (Ec. NUI Galway)

Week 7

14th Mar and 16th Mar

Hands on Experiments

Hands-on experiment 7.1

Build a Python execution strategy that rebalances the
portfolio over 1 year time period based upon ‘profit limit’
and ‘stop loss’ rules calling 1 MAARS strategy (defined at
hands on experiment no 1), while taking into account costs
and slippage and long and short positions.

Hands-on experiment 7.2

Build a Python execution strategy that rebalances the
portfolio over 2 years’ time period based upon ‘profit limit’
and ‘stop loss’ rules calling 4 MAARS strategies
simultaneously (2 Long and 2 Short (defined at hands on
experiment no 1) while taking into account costs and
slippage.

Course Content:

No content available.

Instructors:

All

Week 8/9

20th Mar to 31st Mar

Competition and Prize

Each team is tasked to build own back-testing algo over a 3 year
period that will rebalance the portfolio based upon ‘profit limit’ and
‘stop loss’ rules calling any type of MAARS strategy and any
combination of MAARS strategies, while taking into account costs
and slippage. The goal here is to maximize the return while taking
moderate to level risk.

There is no limit to the course examples: feel free to innovate and
come up with very different ranking strategies and back-testing
algos

You have 2 weeks to experiment and adjust your approach in order
to achieve maximum performance

A 2 page max report required to enter the competition and justify
the profit recorded and the level of risk taken